employee <- read.csv('./data/employees_kr.csv')
head(employee)
## num Incentive Nego Sex Year
## 1 1 12.1 TRUE M 2005
## 2 2 8.9 TRUE F 2006
## 3 3 8.8 TRUE M 2007
## 4 4 7.1 TRUE F 2008
## 5 5 10.2 TRUE M 2009
## 6 6 7.0 TRUE F 2005데이터 시각화 의미와 기법
Exploration
Code
R
Part 4 : 데이터 시각화 의미와 기법
sub2008 <- subset(employee, employee$Year == 2008)
head(sub2008)
## num Incentive Nego Sex Year
## 4 4 7.1 TRUE F 2008
## 9 9 8.2 TRUE M 2008
## 14 14 13.4 TRUE F 2008
## 19 19 8.4 TRUE M 2008
## 24 24 8.0 TRUE F 2008
## 29 29 6.8 TRUE M 2008
hist(sub2008$Incentive)
sub2009 <- subset(employee, employee$Year ==2009)
hist(sub2009$Incentive)
subMan <- subset(employee, employee$Sex == 'M')
hist(subMan$Incentive)
subWoman <- subset(employee, employee$Sex == 'F')
hist(subWoman$Incentive)
subNego <- subset(employee, employee$Nego == "TRUE")
hist(subNego$Incentive)
subNoNego <- subset(employee, employee$Nego == "FALSE")
hist(subNoNego$Incentive)
split.screen(c(2, 1))
## [1] 1 2
screen(1)
plot(1:10)
screen(2)
plot(10:1)
close.screen(all = TRUE)split.screen(c(2, 1))
## [1] 1 2
screen(1)
plot(1:10)
screen(2)
plot(10:1)
screen(2)
plot(1:10)
close.screen(all = TRUE)split.screen(c(2, 3))
## [1] 1 2 3 4 5 6
screen(3)
plot(1:10)
screen(4)
plot(10:1)
screen(1)
plot(1:100)
screen(6)
plot(100:1)
close.screen(all = TRUE)par(mfrow = c(2, 1))
plot(1:10)
plot(10:1)
close.screen(all = TRUE)
## [1] FALSEx <- seq(1, 10, 0.1)
y <- exp(x)
plot(x, y) 
plot(x, y, main = 'Minho Graph',
xlab = 'Time', ylab = 'Income increase')
abc <- c(260, 300, 250, 280, 310)
def <- c(180, 200, 210, 190, 170)
ghi <- c(210, 250, 260, 210, 270)plot(abc, type = 'o', col = 'red',
ylim = c(0, 400), axes = F, ann = F)
# type이 'o'이면 점과 선을 연결해서 꺾은선 그래프가 그려지고, 'p'이면 점만 표시
# color는 red이므로 붉은색
# y축의 범위 0 ~ 400
# axes = F이므로 X, Y축을 표시하지 않음
# ann = F이므로 X, Y축의 이름을 표시하지 않음
axis(1, at = 1:5, lab = c('A', 'B', 'C', 'D', 'E'))
axis(2, ylim = c(0, 400))
title(main = 'Fruit', col.main = 'red', font.main = 4)
title(xlab = 'Day', col.lab = 'black')
title(ylab = 'Price', col.lab = 'blue')
lines(def, type = 'o', pch = 21, col = 'green', lty = 2)
lines(ghi, type = 'o', pch = 22, col = 'blue', lty = 2)
legend(4, 400, c('Orange', 'Appple', 'Banana'), cex = 0.8,
col = c('red', 'green', 'blue'), pch = 21, lty = 1:3)
plot(10:1)
par(new = T)
plot(1:10)
par(new = T)
plot(c(6, 6, 6, 6, 6), type = 'o', col = 'red')
x <- c(50, 40, 32, 68, 120, 92)
barplot(x, names = 'Total Sale Amount')
X_matrix <- matrix(c(50, 40, 32, 68, 120, 92), 3, 2)
X_matrix
## [,1] [,2]
## [1,] 50 68
## [2,] 40 120
## [3,] 32 92
split.screen(c(1, 2))
## [1] 1 2
screen(1)
barplot(X_matrix, names = c('Korea', 'America'))
screen(2)
barplot(X_matrix, names = c('Korea', 'America'), beside = T)
abc <- c(50, 40, 32, 68, 120, 92)
barplot(abc, main = 'abc', xlab = 'Season', ylab = 'Sales',
names.arg = c('A', 'B', 'C', 'D', 'E', 'F'), border = 'blue',
density = c(10, 30, 50, 80, 90, 92))
abc <- c(110, 300, 150, 280, 310)
def <- c(180, 200, 210, 190, 170)
ghi <- c(210, 150, 260, 210, 70)
B_Type2 <- matrix(c(abc, def, ghi), 5, 3)
B_Type2
## [,1] [,2] [,3]
## [1,] 110 180 210
## [2,] 300 200 150
## [3,] 150 210 260
## [4,] 280 190 210
## [5,] 310 170 70
barplot(B_Type2, main = 'Ball Type별 시즌의 판매량',
xlab = 'Ball Type', ylab = '매출', beside = T,
names.arg = c('BaseBall', 'SoccerBall', 'BeachBall'),
border = 'blue', col = rainbow(5), ylim = (c(0, 400)))
legend(16, 400, c('A시즌', 'B시즌', 'C시즌', 'D시즌', 'E시즌'),
cex = 0.8, fill = rainbow(5))
barplot(t(B_Type2), main = '시즌별 볼타입에 따른 판매량',
xlab = 'Season', ylab = 'Price', beside = T,
names.arg = c('A', 'B', 'C', 'D', 'E'),
border = 'blue', col = rainbow(3), ylim = (c(0, 400)))
legend(16, 400, c('BaseBall', 'SoccerBall', 'BeachBall'),
cex = 0.8, fill = rainbow(5))
barplot(t(B_Type2), main = '시즌별 볼타입에 따른 판매량(누적 표시형)',
xlab = 'Season', ylab = '매출',
names.arg = c('A', 'B', 'C', 'D', 'E'),
border = 'blue', col = rainbow(3), ylim = (c(0, 1000)))
legend(4.5, 1000, c('BaseBall', 'SoccerBall', 'BeachBall'),
cex = 0.8, fill = rainbow(3))
x
## [1] 50 40 32 68 120 92
dotchart(x, labels = c('A', 'B', 'C', 'D', 'E', 'F'), pch = 22)
x <- c(1, 2, 1, 4, 5, 4, 5, 2, 3, 5, 2, 6, 7, 3,
7, 8, 6, 5, 4, 7, 7, 6, 5, 7, 8, 9, 8)
hist(x, xlim = c(0, 10), ylim = c(0, 6),
nclass = 12, main = 'Call number of #1 Topic')
T_sales <- c(210, 110, 400, 550, 700, 130)
pie(T_sales)
pie(T_sales, init.angle = 90, col = rainbow(length(T_sales)),
labels = c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'))
legend(1, 1, c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'),
cex = 0.8, fill = rainbow(length(T_sales)))
library(plotrix)
T_sales <- c(210, 110, 400, 550, 700, 130)
week <- c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday')
ratio <- round(T_sales / sum(T_sales) * 100, 1)
label <- paste(week, '\n', ratio, '%')
pie3D(T_sales, main = '주간 매출 변동',
col = rainbow(length(T_sales)), cex = 0.8, labels = label)
abc ; def; ghi
## [1] 110 300 150 280 310
## [1] 180 200 210 190 170
## [1] 210 150 260 210 70
boxplot(abc, def, ghi) 
boxplot(abc, def, ghi, col = c('yellow', 'cyan', 'green'),
names = c('BaseBall', 'SoccerBall', 'BeachBall'),
horizontal = TRUE)
plot(1:10)
par(new = T)
plot(10:1)
plot.new()
plot(1:100)
plot.new()
plot(-4:4,-4:4, type = 'n')
points(rnorm(200), rnorm(200), pch = '+', col = 'red')
par(new = T)
points(rnorm(200), rnorm(200), pch = 'o', col = 'cyan') 
x <- c(1:10)
y <- x * x
plot(x, y, type = 'n', main = 'Title')
for (i in 1:5) lines(x, (y + i * 5), col = i, lty = i)
x <- c(1, 3, 6, 8, 9)
y <- c(12, 56, 78, 32, 9)
plot(x, y)
segments(6, 78, 8, 32)
arrows(3, 56, 1, 12)
rect(4, 20, 6, 30, density = 3)
text(4, 40, '이것은 샘플입니다', srt = 55)
mtext('상단의 문자열입니다', side = 3)
mtext('우측의 문자열입니다', side = 4, adj = 0.3)
box(lty = 2, col = 'red')
axis(1, pos = 40, at = 0:10, col = 2)
axis(2, pos = 5, at = 10:60)
x <- c(1:10)
y <- exp(x)
plot(x, y, type = 'n', main = 'Title')
for (i in 1:10) lines(x, (y + i * 5), col = i, lty = i)
y <- x * x
plot(x, y, type = 'n', main = 'Title')
for (i in 1:10) lines(x, (y + i * 5), col = i, lty = i)
mtext('Right Side Text', side = 4, adj = 0.5)
abline(1:2)
box(lty = 2, col = 'red')
axis(1, pos = 50, at = 0:10, col = 2)
axis(2, pos = 6, at = 0:100, col = 3)
x <- c(1, 2, 1, 4, 5, 4, 5, 2, 3, 5, 2, 6, 7, 3, 7, 8, 6, 5, 4, 7, 7, 6, 5, 7, 8, 9, 8)
par(mfrow = c(1, 2))
hist(x)
hist(x, probability = T, main = 'Histogram with density line')
lines(density(x))
x1 = seq(-3, 3, length = 50)
x2 <- seq(-4, 4, length = 60)
f <- function(x1, x2) {
x1 ^ 2 + x2 ^ 2 + x1 * x2
}
y = outer(x1, x2, FUN = f)
persp(x1, x2, y)
# install.packages('scatterplot3d')
plot3DfishData <- function(x, y, z, data = iris)
{
require('scatterplot3d')
fish.variable <- colnames(data)
scatterplot3d(
data[, x],
data[, y],
data[, z],
color = c('blue', 'black', 'red', 'green', 'turquoise')
[data$Species]
,
pch = 19,
xlab = fish.variable[x],
ylab = fish.variable[y],
zlab = fish.variable[z]
)
}
par(mfrow = c(2, 2))
plot3DfishData(1, 2, 5)
## 필요한 패키지를 로딩중입니다: scatterplot3d
plot3DfishData(1, 2, 3)
plot3DfishData(3, 4, 5)
plot3DfishData(2, 3, 5) 
# install.packages('lattice')
library(lattice)
data(quakes)
head(quakes)
## lat long depth mag stations
## 1 -20.42 181.62 562 4.8 41
## 2 -20.62 181.03 650 4.2 15
## 3 -26.00 184.10 42 5.4 43
## 4 -17.97 181.66 626 4.1 19
## 5 -20.42 181.96 649 4.0 11
## 6 -19.68 184.31 195 4.0 12
str(quakes)
## 'data.frame': 1000 obs. of 5 variables:
## $ lat : num -20.4 -20.6 -26 -18 -20.4 ...
## $ long : num 182 181 184 182 182 ...
## $ depth : int 562 650 42 626 649 195 82 194 211 622 ...
## $ mag : num 4.8 4.2 5.4 4.1 4 4 4.8 4.4 4.7 4.3 ...
## $ stations: int 41 15 43 19 11 12 43 15 35 19 ...
mini <- min(quakes$depth)
maxi <- max(quakes$depth)
mini
## [1] 40
maxi
## [1] 680
r <- ceiling((maxi - mini) / 8)
inf <- seq(mini, maxi, r)
r
## [1] 80
inf
## [1] 40 120 200 280 360 440 520 600 680
quakes$depth.cat <- factor(floor((quakes$depth - mini) / r), labels = paste(inf, inf + r, sep = '-'))
xyplot(lat ~ long | depth.cat, data = quakes, main = 'EarthQuake Data')
cloud(mag ~ lat * long, data = quakes, sub = 'Magnitude With Longitude and Lattide')
bwplot(mag ~ depth.cat, data = quakes, main = 'Depth and Strength Relationship')
cloud(Species ~ Sepal.Length + Petal.Length, data = iris, main = 'IRIS Data View')
splom(iris[, 1:4])
bwplot(Sepal.Length ~ Sepal.Width, data = iris)
# install.packages('ggplot2')
library(ggplot2)
diamonds
## # A tibble: 53,940 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
## # ℹ 53,930 more rows
g <- diamonds[order(diamonds$table),]
head(g)
## # A tibble: 6 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 1.04 Ideal I VS1 62.9 43 4997 6.45 6.41 4.04
## 2 0.29 Very Good E VS1 62.8 44 474 4.2 4.24 2.65
## 3 1 Fair I VS1 64 49 3951 6.43 6.39 4.1
## 4 0.3 Fair E SI1 64.5 49 630 4.28 4.25 2.75
## 5 1.02 Fair F SI1 61.8 50 4227 6.59 6.51 4.05
## 6 2 Fair H SI1 61.2 50 13764 8.17 8.08 4.97
tail(g)
## # A tibble: 6 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.71 Fair D VS2 55.6 73 2368 6.01 5.96 3.33
## 2 0.5 Fair E VS2 79 73 2579 5.21 5.18 4.09
## 3 0.5 Fair E VS2 79 73 2579 5.21 5.18 4.09
## 4 0.79 Fair G SI1 65.3 76 2362 5.52 5.13 3.35
## 5 0.81 Fair F SI2 68.8 79 2301 5.26 5.2 3.58
## 6 2.01 Fair F SI1 58.6 95 13387 8.32 8.31 4.87gg <- ggplot(diamonds, aes(x = carat, y = price))
gg + geom_point()
gg <- ggplot(diamonds, aes(x = carat, y = price))
gg + geom_point(size = 1, shape = 2, color = 'steelblue', stroke = 1)
gg <- ggplot(diamonds, aes(x = carat, y = price))
gg + geom_point(aes(size = carat, shape = cut, color = color, stroke = carat))
## Warning: Using shapes for an ordinal variable is not advised
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat Layer', y = 'Price Layer')
print(gg2)
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat', y = 'Price')
gg2 + theme(text = element_text(color = 'red'))
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat', y = 'Price')
gg3 <- gg2 + theme(plot.title = element_text(size = 25),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15))
print(gg3)
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat', y = 'Price')
gg3 <- gg2 + theme(plot.title = element_text(size = 25),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15))
print(gg3)
gg3 + labs(title = 'Plot Title \nSecond Line of Plot Title') +
theme(plot.title = element_text(face = 'bold', color = 'steelblue', lineheight = 1.2))
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat', y = 'Price')
gg3 <- gg2 + theme(plot.title = element_text(size = 25),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15))
print(gg3)
gg3 + scale_colour_manual(name = 'Legend',
values = c(
'D' = 'grey',
'E' = 'red',
'F' = 'blue',
'G' = 'yellow',
'H' = 'black',
'I' = 'green',
'J' = 'firebrick'))
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat', y = 'Price')
gg3 <- gg2 + theme(plot.title = element_text(size = 25),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15))
print(gg3)
gg3 + coord_cartesian(xlim = c(0, 3), ylim = c(0, 5000)) + geom_smooth()
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat', y = 'Price')
gg3 <- gg2 + theme(plot.title = element_text(size = 25),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15))
print(gg3)
gg3 + coord_flip()
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat', y = 'Price')
gg3 <- gg2 + theme(plot.title = element_text(size = 25),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15))
print(gg3)
gg3 + theme(plot.background = element_rect(fill = 'yellowgreen'),
plot.margin = unit(c(2, 4, 1, 3), 'cm'))
gg1 <- gg + geom_point(aes(color = color))
gg2 <- gg1 + labs(title = 'Diamonds', x = 'Carat', y = 'Price')
gg3 <- gg2 + theme(plot.title = element_text(size = 25),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15))
print(gg3)
p1 <- gg3 + geom_hline(yintercept = 5000, size = 2, linetype = 'dotted', color = 'blue')
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
print(p1)
options(scipen = 999) # turn-off scientific notation like 1e+48
library(ggplot2)
theme_set(theme_bw()) # pre-set the bw theme.
data('midwest', package = 'ggplot2')
# Scatterplot
gg <- ggplot(midwest, aes(x = area, y = poptotal)) +
geom_point(aes(col = state, size = popdensity)) +
geom_smooth(method = 'loess', se = F) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) +
labs(subtitle = 'Area Vs Population',
y = 'Population',
x = 'Area',
title = 'Scatterplot',
caption = 'Source: midwest')
plot(gg)
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 15 rows containing missing values (`geom_point()`).
library(ggplot2)
data(mpg, package = 'ggplot2') # alternate source: 'http://goo.gl/uEeRGu')
theme_set(theme_bw()) # pre-set the bw theme.
g <- ggplot(mpg, aes(cty, hwy))
# Scatterplot 그림을 그린다. 다음 내용은 배치로 만든 후에 실행할 것
g + geom_point() +
geom_smooth(method = 'lm', se = F) +
labs(subtitle = 'mpg: city vs highway mileage',
y = 'hwy',
x = 'cty',
title = 'Scatterplot with overlapping points',
caption = 'Source: midwest')
## `geom_smooth()` using formula = 'y ~ x'
library(ggplot2)
data(mpg, package = 'ggplot2')
# Scatterplot
theme_set(theme_bw()) #pre-set the bw theme.
g <- ggplot(mpg, aes(cty, hwy))
g + geom_count(col = 'tomato3', show.legend = F) +
labs(subtitle = 'mpg: city vs highway mileage',
y = 'hwy',
x = 'cty',
title = 'Counts Plot')
library(ggplot2)
# install.packages('ggExtra')
library(ggExtra)
data(mpg, package = 'ggplot2')
# Scatterplot
theme_set(theme_bw())
# pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27,]
g <- ggplot(mpg, aes(cty, hwy)) +
geom_count() +
geom_smooth(method = 'lm', se = F)
ggMarginal(g, type = 'histogram', fill = 'transparent')
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
ggMarginal(g, type = 'boxplot', fill = 'transparent')
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
## Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
library(ggplot2)
# install.packages('ggcorrplot')
library(ggcorrplot)
# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
# Plot
ggcorrplot(corr,
hc.order = TRUE,
type = 'lower',
lab = TRUE,
lab_size = 3,
method = 'circle',
colors = c('tomato2', 'white', 'springgreen3'),
title = 'Correlogram of mtcars',
ggtheme = theme_bw)
library(ggplot2)
theme_set(theme_bw())
data('mtcars') # 데이터를 읽는다.
mtcars$`car name` <- rownames(mtcars) # 차 이름을 위한 칼럼을 만든다.
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg)) / sd(mtcars$mpg), 2)
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, 'below', 'above')
mtcars <- mtcars[order(mtcars$mpg_z), ] # 정렬한다.
mtcars$'car name' <- factor(mtcars$'car name', levels = mtcars$'car name')
ggplot(mtcars, aes(x = `car name`, y = mpg_z, label = mpg_z)) +
geom_bar(stat = 'identity', aes(fill = mpg_type), width = .5) +
scale_fill_manual(name = 'Mileage',
labels = c('Above Average', 'Below Average'),
values = c('above' = '#00ba38', 'below' = '#f8766d')) +
labs(subtitle = 'Normalised mileage from "mtcars"',
title = 'Diverging Bars') +
coord_flip()
library(ggplot2)
theme_set(theme_bw())
# plot
g <- ggplot(mpg, aes(class, cty))
g + geom_violin() +
labs(title = 'Violin plot',
subtitle = 'City Mileage vs Class of vehicle',
caption = 'Source: mpg',
x = 'Class of Vehicle',
y = 'City Mileage')
# install.packages('ggplot2')
library(ggplot2)
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaqplot(Sepal.Length, Petal.Length, data = iris)
## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width)
qplot(Sepal.Length, Petal.Length, data = iris, geom = 'line', color = Species)
qplot(age, circumference, data = Orange, geom = 'line', colour = Tree, main = 'How does orange tree circumference vary with age?')
Titanic
## , , Age = Child, Survived = No
##
## Sex
## Class Male Female
## 1st 0 0
## 2nd 0 0
## 3rd 35 17
## Crew 0 0
##
## , , Age = Adult, Survived = No
##
## Sex
## Class Male Female
## 1st 118 4
## 2nd 154 13
## 3rd 387 89
## Crew 670 3
##
## , , Age = Child, Survived = Yes
##
## Sex
## Class Male Female
## 1st 5 1
## 2nd 11 13
## 3rd 13 14
## Crew 0 0
##
## , , Age = Adult, Survived = Yes
##
## Sex
## Class Male Female
## 1st 57 140
## 2nd 14 80
## 3rd 75 76
## Crew 192 20
str(Titanic)
## 'table' num [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154 ...
## - attr(*, "dimnames")=List of 4
## ..$ Class : chr [1:4] "1st" "2nd" "3rd" "Crew"
## ..$ Sex : chr [1:2] "Male" "Female"
## ..$ Age : chr [1:2] "Child" "Adult"
## ..$ Survived: chr [1:2] "No" "Yes"
mosaicplot(Titanic, main = 'Titanic Data, Class,Sex,Age,Survival', col = TRUE)